Born in Tokyo, grew up in Mexico, and studied in the US. Love food, nature, tech, and culture. Beckhoff Japan Managing Director, SR at KEIO SFC, Haas MBA, UT BS
🚨 BREAKING:
Microsoft's first robotics foundation model! 🤯
Microsoft just announced Rho-alpha (ρα), their first robotics model derived from the Phi series of vision-language models.
Rho-alpha translates natural language commands into control signals for robotic systems performing bimanual manipulation tasks. Commands like "push the green button with the right gripper," "pull out the red wire," "flip the top switch on," or "turn the knob to position 5" get executed directly by dual-arm robots.
What makes this different from standard vision-language-action (VLA) models is the additional modalities. Rho-alpha is a VLA+ model that adds tactile sensing to the perceptual mix, with plans to incorporate force feedback.
On the learning side, the model is designed to continually improve during deployment by learning from human feedback.
The training approach combines trajectories from physical demonstrations and simulated tasks with web-scale visual question answering data.
Since teleoperation data is scarce and expensive, Microsoft is using NVIDIA Isaac Sim on Azure to generate physically accurate synthetic datasets via reinforcement learning. These simulated trajectories get combined with commercial and open physical demonstration datasets.
The model is currently under evaluation on dual-arm setups and humanoid robots. Microsoft is opening an Early Access Program for organizations interested in evaluating Rho-alpha.
Robots that can adapt to dynamic situations and human preferences are more useful in real environments and more trusted by the people operating them.
Read more here: https://t.co/BQVMdeFyuV
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🤖 Introducing InternVLA-A1 — now fully open-sourced!
Many VLA models follow instructions well in static scenes… but struggle in dynamic environments (conveyor belts, rotating platforms, multi-robot setups). Why? They see the present—but can’t imagine the future.
InternVLA-A1 solution: unify perception, imagination, and action in one model:
✅ Scene understanding: Image + text → task parsing
✅ Task imagination: Predict future frames → reason about dynamics
✅ Guided control: Execute actions steered by visual foresight
Powered by InternData-A1 - Large-scale high-quality simulated dataset, InternVLA-A1 stays robust under complex backgrounds, lighting, and distractions.
🔥 See it in action:
1️⃣ High-speed conveyor: track, predict, and stably grasp or flip packages
2️⃣ Rotating platform: task-aware recognition & precise pick-up of diverse items
📊 Outperforms π0 and Gr00t N1.5 on general manipulation benchmarks!
✨ Model, data, and code are all open!
Models: https://t.co/5PYmtZNUoO
Datasets: https://t.co/Ipi9XF6sw7
GitHub: https://t.co/AddhXUyugu
Disney Research shared a new look at the Olaf animated character, included a closer look at the inner workings of the robot, walk cycle, impact reductions, and how they track the character's performance.
Spiderman's stuntman is a robot! 🕸️
The Walt Disney Company Imagineers designed an advanced robotics figure that flies 25 meters in the air making its own real-time decisions as it tucks, somersaults, slows down, and climbs. 🤹🏼♀️
The result is Spider-Man in Avengers Campus, flying above with gravity-defying feats never before seen in a Disney park.
That's incredible to see how robotics is changing different verticals!
That's pure magic to me! 🔮
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In a new Google DeepMind video, Hannah Fry threw a curveball into the scene: a squishy stress ball the model had never encountered.
Watch it adapt to the physics in real-time. This is what open-ended robotics looks like.